code: def save_model(self, output):
      torch.save(self.actor.state_dict(), "{}/actor.pkl".format(output))
      torch.save(
          self.critic.state_dict(), "{}/critic.pkl".format(output)
      )
  
imports: import numpy
  import torch
  import torch.nn
  from torch.optim import Adam
  from .memory import SequentialMemory
  from .utils import to_numpy, to_tensor
label_desc: Use image node_id 10 for calling the DDPG obj's underlying member method code with example usage: obj.save_model(output) without return types